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While humans can effortlessly transform complex visual scenes into simplewords and the other way around by leveraging their high-level understanding ofthe content, conventional or the more recent learned image compression codecsdo not seem to utilize the semantic meanings of visual content to their fullpotential. Moreover, they focus mostly on rate-distortion and tend tounderperform in perception quality especially in low bitrate regime, and oftendisregard the performance of downstream computer vision algorithms, which is afast-growing consumer group of compressed images in addition to human viewers.In this paper, we (1) present a generic framework that can enable any imagecodec to leverage high-level semantics and (2) study the joint optimization ofperception quality and distortion. Our idea is that given any codec, we utilizehigh-level semantics to augment the low-level visual features extracted by itand produce essentially a new, semantic-aware codec. We propose a three-phasetraining scheme that teaches semantic-aware codecs to leverage the power ofsemantic to jointly optimize rate-perception-distortion (R-PD) performance. Asan additional benefit, semantic-aware codecs also boost the performance ofdownstream computer vision algorithms. To validate our claim, we performextensive empirical evaluations and provide both quantitative and qualitativeresults.

JPD-SE: High-Level Semantics for Joint Perception-Distortion Enhancement in Image Compression